Predicting the species abundance distribution using a model food web

TL;DR

This paper uses an ecosystem model to analyze species abundance distributions, demonstrating that the power-law normal distribution outperforms traditional models and offers detailed insights into ecological data.

Contribution

It introduces a model-based approach to distinguish between SAD models and identifies the power-law normal distribution as superior for ecological data.

Findings

Power-law normal distribution outperforms log-normal and logit-normal models.

Empirical data reveals features distinguishing SAD models.

Data can be improved at the high-population cut-off.

Abstract

A large number of models of the species abundance distribution (SAD) have been proposed, many of which are generically similar to the log-normal distribution, from which they are often indistinguishable when describing a given data set. Ecological data sets are necessarily incomplete samples of an ecosystem, subject to statistical noise, and cannot readily be combined to yield a closer approximation to the underlying distribution. In this paper we use empirical data obtained from an ecosystem model to study the predicted SAD in detail, resolving features which can distinguish between models but which are poorly seen in field data. We find that the power-law normal distribution is superior to both the log-normal and logit-normal distributions, and that the data can improve on even this at the high-population cut-off.